Electronic Science and Technology ›› 2021, Vol. 34 ›› Issue (4): 75-82.doi: 10.16180/j.cnki.issn1007-7820.2021.04.012

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Bearing Fault Diagnosis Algorithm Based on One-Dimensional WConv-BiLSTM

YAN Shuhao1,QIAO Meiying1,2   

  1. 1. School of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo 454000,China
    2. Collaborative Innovation Center of Coal Work Safety,Jiaozuo 454000,China
  • Received:2019-12-19 Online:2021-04-15 Published:2021-04-16
  • Supported by:
    National Natural Science Foundation of China(61573129);Natural Science Foundation of China-Henan(172102310239)


In view of bearing fault diagnosis under different signal-to-noise ratio noise interference, a bearing fault diagnosis model based on one-dimensional wide convolution kernel convolutional neural network and bidirectional long short-term memory neural network is established. The bearing vibration signals are used as input to the proposed model, and the short-time Fourier transform is used to transform the vibration signal into the time-frequency figures. Convolutional neural networks with wide convolution kernels and bidirectional long short-term memory neural networks are applied to extract the spatial and temporal characteristics of data, and classification is achieved through dense layers. Data augmentation, mini-batch, and batch normalization methods are utilized to enhance the anti-noise performance of the model. The test uses the CWRU bearing fault data set, and adds noise with different SNR to construct noise interference tests. The experiments show that proposed model has great noise resistance, and can achieve 98% accuracy of fault recognition with a signal-to-noise ratio of 2 dB.

Key words: bearing fault diagnosis, CNN, wide convolution kernels, BiLSTM, short-time Fourier transform, noise immunity

CLC Number: 

  • TP389.1